Abstract-Smart meter deployments are spurring renewed interest in analysis techniques for electricity usage data. An important prerequisite for data analysis is characterizing and modeling how electrical loads use power. While prior work has made significant progress in deriving insights from electricity data, one issue that limits accuracy is the use of general and often simplistic load models. Prior models often associate a fixed power level with an "on" state and either no power, or some minimal amount, with an "off" state. This paper's goal is to develop a new methodology for modeling electric loads that is both simple and accurate. Our approach is empirical in nature: we monitor a wide variety of common loads to distill a small number of common usage characteristics, which we leverage to construct accurate load-specific models. We show that our models are significantly more accurate than binary on-off models, decreasing the root mean square error by as much as 8X for representative loads. Finally, we demonstrate two example uses of our models in data analysis: i) generating device-accurate synthetic traces of building electricity usage, and ii) filtering out loads that generate rapid and random power variations in building electricity data.
Utilities are rapidly deploying smart meters that measure electricity usage in real-time. Unfortunately, smart meters indirectly leak sensitive information about a home's occupancy, which is easy to detect because it highly correlates with simple statistical metrics, such as power's mean, variance, and range. To prevent occupancy detection, we propose using the thermal energy storage of electric water heaters already present in many homes. In essence, our approach, which we call combined heat and privacy (CHPr), modulates a water heater's power usage to make it look like someone is always home. We design a CHPr-enabled water heater that regulates its energy usage to thwart a variety of occupancy detection attacks without violating its objective-to provide hot water on demand-and evaluate it in simulation using real data. Our results show that a standard 50-gal CHPr-enabled water heater prevents a wide range of state-of-the-art occupancy detection attacks.
Heating, ventilation, and air conditioning (HVAC) accounts for over 50% of a typical home’s energy usage. A thermostat generally controls HVAC usage in a home to ensure user comfort. In this article, we focus on making existing “dumb” programmable thermostats smart by applying energy analytics on smart meter data to infer home occupancy patterns and compute an optimized thermostat schedule. Utilities with smart meter deployments are capable of immediately applying our approach, called iProgram, to homes across their customer base. iProgram addresses new challenges in inferring home occupancy from smart meter data where (i) training data is not available and (ii) the thermostat schedule may be misaligned with occupancy, frequently resulting in high power usage during unoccupied periods. iProgram translates occupancy patterns inferred from opaque smart meter data into a custom schedule for existing types of programmable thermostats, e.g., 1-day, 7-day, and so on. We implement iProgram as a web service and show that it reduces the mismatch time between the occupancy pattern and the thermostat schedule by a median value of 44.28min (out of 100 homes) when compared to a default 8am-6pm weekday schedule, with a median deviation of 30.76min off the optimal schedule. Further, iProgram yields a daily energy savings of 0.42kWh on average across the 100 homes. Moreover, the schedules generated from iProgram converge to optimal schedules within a couple of weeks for most homes. We also show that homeowners having multiple HVAC zones can utilize iProgram and potentially increase unconditioned times of less occupied parts of their homes by 70%. Utilities may use iProgram to recommend thermostat schedules to customers and provide them estimates of potential energy savings in their energy bills.
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